This forces a full reinstall of all homebrew packages, fixing the dyld: Library not loaded errors when trying to run git, psql, node etc. This often happens after using Apple's Migration Assistant. Also can be caused by macOS updates.
brew list -1 > brew.txt # list out all installed packages
brew list -1 | xargs brew rm --force # remove all installed packages
brew install $(cat brew.txt | tr '\n' ' ') # install all previously installed packages
| Please summarize the paper. Follow these two steps. | |
| ## Step 1 | |
| Act as a curious, meticulous reader with attention to detail, objectivity, precision and sensitivity to novelty. Your job is to: | |
| * Summarize each and every (!!!) key point/insight. Do not miss any; if there are many key points/insights, list them all regardless of length. | |
| * Each point/insight must come with rich, precise, specific (!!!) details (e.g., numbers); details are really important. | |
| * Each point/insight must be supported with direct quotes (!!!). Do not use quotes to simply repeat the point; instead, embed them naturally within your summary. Quotes should be used to better present the points (see the example below). | |
| * If two points/insights are redundant, consider combining or integrating them. Be concise, but do not miss key points, insights, or details. | |
| * If the text has sections (e.g., an academic paper), proceed section by section (e.g., focus on the first, then the second and so on), with each summary section starting with the origin |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| /** | |
| * Shelly Pro 3EM - Net Metering (Saldierung) & Home Assistant Auto-Discovery | |
| * Version: 1.1.8 | |
| * | |
| * DISCLAIMER: | |
| * Use this script entirely at your own risk! I assume absolutely no liability | |
| * for any direct, indirect, or consequential damages. This includes, but is | |
| * not limited to, damage to the Shelly device, any connected electrical | |
| * equipment, other devices in your network, data loss, or system malfunctions. | |
| * By using this script, you acknowledge that you alone are responsible for |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
| <domain type='kvm' xmlns:qemu='http://libvirt.org/schemas/domain/qemu/1.0'> | |
| <!-- ... --> | |
| <qemu:commandline> | |
| <qemu:arg value='-acpitable'/> | |
| <qemu:arg value='file=/some/path/slic.bin'/> | |
| <qemu:arg value='-acpitable'/> | |
| <qemu:arg value='file=/some/path/msdm.bin'/> | |
| <qemu:arg value='-smbios'/> | |
| <qemu:arg value='file=/some/path/smbios_type_0.bin'/> | |
| <qemu:arg value='-smbios'/> |